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Polarization adds another dimension to the spatial intensity and spectral information typically acquired in remote sensing. Polarization imparted by surface reflections contains unique and discriminatory signatures which may augment spectral target-detection techniques. Benefits such as improving man-made object detection are often touted, as well as… (More)

This paper describes a collaborative collection campaign to spectrally image and measure a well characterized scene for hyperspectral algorithm development and validation/verification of scene simulation models (DIRSIG). The RIT Megascene, located in the northeast corner of Monroe County near Rochester, New York, has been modeled and characterized under the… (More)

The ability to detect and identify effluent gases is, and will continue to be, of great importance. This would not only aid in the regulation of pollutants but also in treaty enforcement and monitoring the production of weapons. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research… (More)

- David W. Messinger, James Albano
- 2011 3rd Workshop on Hyperspectral Image and…
- 2011

For many applications in spectral image analysis, the quantitative model used to describe the data is based on first and second order statistics, linear mixture models (i.e., the convex hull), and / or linear subspaces. An example of this for anomaly detection is the well known RX algorithm, a statistical measure of the anomalousness of individual pixels… (More)

- Kelly Canham, Ariel Schlamm, Amanda Ziemann, Bill Basener, David W. Messinger
- IEEE Trans. Geoscience and Remote Sensing
- 2011

Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process.… (More)

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or… (More)

- David W. Messinger, Amanda Ziemann, Ariel Schlamm, Bill Basener
- 2010 2nd Workshop on Hyperspectral Image and…
- 2010

Most spectral image processing schemes develop models of the data in the hyperspace by using first and second order statistics or linear subspace geometries applied to the image globally. However, it is simple to show that the data are typically not multivariate Gaussian or are not well defined by linear geometries when considering the entire image,… (More)

Spectral image complexity is an ill-defined term that has been addressed previously in terms of dimensionality, multivariate normality, and other approaches. Here, we apply the concept of the linear mixture model to the question of spectral image complexity at spatially local scales. Essentially, the " complexity " of an image region is related to the… (More)

Spectral image analysis problems often begin by performing a preprocessing step composed of applying a transformation that generates an alternative representation of the spectral data. In this paper, a transformation based on a Markov-chain model of a random walk on a graph is introduced. More precisely, we quantify the randomwalk using a quantity known as… (More)

- DAVID W. MESSINGER, NATALIE M. SINISGALLI
- 2007

Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and… (More)